Overview

Dataset statistics

Number of variables48
Number of observations13348
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory160.6 B

Variable types

Numeric10
Categorical38

Alerts

Genres_Action is highly imbalanced (70.2%)Imbalance
Genres_Adventure is highly imbalanced (87.1%)Imbalance
Genres_Board is highly imbalanced (73.1%)Imbalance
Genres_Books is highly imbalanced (99.4%)Imbalance
Genres_Business is highly imbalanced (99.4%)Imbalance
Genres_Card is highly imbalanced (86.5%)Imbalance
Genres_Casino is highly imbalanced (98.2%)Imbalance
Genres_Casual is highly imbalanced (82.8%)Imbalance
Genres_Education is highly imbalanced (90.6%)Imbalance
Genres_Emoji&Expressions is highly imbalanced (99.9%)Imbalance
Genres_Family is highly imbalanced (88.2%)Imbalance
Genres_Finance is highly imbalanced (99.0%)Imbalance
Genres_Food&Drink is highly imbalanced (98.7%)Imbalance
Genres_Games is highly imbalanced (88.8%)Imbalance
Genres_Gaming is highly imbalanced (99.8%)Imbalance
Genres_Health&Fitness is highly imbalanced (99.1%)Imbalance
Genres_Kids&Family is highly imbalanced (99.9%)Imbalance
Genres_Lifestyle is highly imbalanced (96.3%)Imbalance
Genres_Medical is highly imbalanced (99.8%)Imbalance
Genres_Music is highly imbalanced (97.6%)Imbalance
Genres_Navigation is highly imbalanced (99.8%)Imbalance
Genres_News is highly imbalanced (99.4%)Imbalance
Genres_Photo&Video is highly imbalanced (99.4%)Imbalance
Genres_Productivity is highly imbalanced (99.2%)Imbalance
Genres_Puzzle is highly imbalanced (62.0%)Imbalance
Genres_Racing is highly imbalanced (97.6%)Imbalance
Genres_Reference is highly imbalanced (98.4%)Imbalance
Genres_RolePlaying is highly imbalanced (75.1%)Imbalance
Genres_Simulation is highly imbalanced (67.6%)Imbalance
Genres_SocialNetworking is highly imbalanced (96.9%)Imbalance
Genres_Sports is highly imbalanced (92.2%)Imbalance
Genres_Travel is highly imbalanced (97.8%)Imbalance
Genres_Trivia is highly imbalanced (95.8%)Imbalance
Genres_Utilities is highly imbalanced (98.2%)Imbalance
Genres_Word is highly imbalanced (97.7%)Imbalance
User Rating Count is highly skewed (γ1 = 45.66058631)Skewed
Price is highly skewed (γ1 = 32.8776753)Skewed
Price has 10990 (82.3%) zerosZeros
In-app Purchases has 5238 (39.2%) zerosZeros

Reproduction

Analysis started2023-04-18 10:09:06.115630
Analysis finished2023-04-18 10:09:23.594191
Duration17.48 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

User Rating Count
Real number (ℝ)

Distinct1410
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3823.8867
Minimum5
Maximum3032734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.6 KiB
2023-04-18T12:09:23.684197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q113
median50
Q3338
95-th percentile9116
Maximum3032734
Range3032729
Interquartile range (IQR)325

Descriptive statistics

Standard deviation53114.676
Coefficient of variation (CV)13.890233
Kurtosis2457.0948
Mean3823.8867
Median Absolute Deviation (MAD)43
Skewness45.660586
Sum51041240
Variance2.8211688 × 109
MonotonicityNot monotonic
2023-04-18T12:09:23.839045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 689
 
5.2%
6 530
 
4.0%
7 417
 
3.1%
8 406
 
3.0%
12 288
 
2.2%
11 283
 
2.1%
9 282
 
2.1%
10 271
 
2.0%
13 264
 
2.0%
15 214
 
1.6%
Other values (1400) 9704
72.7%
ValueCountFrequency (%)
5 689
5.2%
6 530
4.0%
7 417
3.1%
8 406
3.0%
9 282
2.1%
10 271
 
2.0%
11 283
2.1%
12 288
2.2%
13 264
 
2.0%
14 206
 
1.5%
ValueCountFrequency (%)
3032734 3
< 0.1%
1277095 3
< 0.1%
711409 2
< 0.1%
439776 3
< 0.1%
400787 2
< 0.1%
374772 3
< 0.1%
283035 3
< 0.1%
273687 3
< 0.1%
257852 3
< 0.1%
240990 2
< 0.1%

Price
Real number (ℝ)

SKEWED  ZEROS 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60364249
Minimum0
Maximum139.99
Zeros10990
Zeros (%)82.3%
Negative0
Negative (%)0.0%
Memory size221.6 KiB
2023-04-18T12:09:23.976811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.99
Maximum139.99
Range139.99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6765916
Coefficient of variation (CV)4.4340676
Kurtosis1656.0998
Mean0.60364249
Median Absolute Deviation (MAD)0
Skewness32.877675
Sum8057.42
Variance7.1641429
MonotonicityNot monotonic
2023-04-18T12:09:24.084790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.0 10990
82.3%
0.99 703
 
5.3%
2.99 467
 
3.5%
1.99 398
 
3.0%
4.99 334
 
2.5%
3.99 208
 
1.6%
9.99 74
 
0.6%
5.99 53
 
0.4%
6.99 37
 
0.3%
8.99 34
 
0.3%
Other values (7) 50
 
0.4%
ValueCountFrequency (%)
0.0 10990
82.3%
0.99 703
 
5.3%
1.99 398
 
3.0%
2.99 467
 
3.5%
3.99 208
 
1.6%
4.99 334
 
2.5%
5.99 53
 
0.4%
6.99 37
 
0.3%
7.99 17
 
0.1%
8.99 34
 
0.3%
ValueCountFrequency (%)
139.99 3
 
< 0.1%
19.99 15
 
0.1%
16.99 2
 
< 0.1%
14.99 1
 
< 0.1%
12.99 7
 
0.1%
11.99 5
 
< 0.1%
9.99 74
0.6%
8.99 34
0.3%
7.99 17
 
0.1%
6.99 37
0.3%

In-app Purchases
Real number (ℝ)

Distinct1197
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.557248
Minimum0
Maximum674.9
Zeros5238
Zeros (%)39.2%
Negative0
Negative (%)0.0%
Memory size208.6 KiB
2023-04-18T12:09:24.359593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.98
Q336.93
95-th percentile200.9
Maximum674.9
Range674.9
Interquartile range (IQR)36.93

Descriptive statistics

Standard deviation70.193436
Coefficient of variation (CV)1.9200963
Kurtosis8.4874211
Mean36.557248
Median Absolute Deviation (MAD)2.98
Skewness2.6685445
Sum487966.15
Variance4927.1184
MonotonicityNot monotonic
2023-04-18T12:09:24.503997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5238
39.2%
0.99 724
 
5.4%
1.99 473
 
3.5%
2.99 367
 
2.7%
1.98 136
 
1.0%
4.99 105
 
0.8%
5.98 85
 
0.6%
2.98 75
 
0.6%
2.97 73
 
0.5%
7.97 65
 
0.5%
Other values (1187) 6007
45.0%
ValueCountFrequency (%)
0 5238
39.2%
0.99 724
 
5.4%
1.49 3
 
< 0.1%
1.98 136
 
1.0%
1.99 473
 
3.5%
2.49 3
 
< 0.1%
2.97 73
 
0.5%
2.98 75
 
0.6%
2.99 367
 
2.7%
3.96 22
 
0.2%
ValueCountFrequency (%)
674.9 3
< 0.1%
654.9 3
< 0.1%
474.9 2
< 0.1%
461.87 2
< 0.1%
434.86 3
< 0.1%
429.9 2
< 0.1%
427.9 2
< 0.1%
410.92 3
< 0.1%
400.33 2
< 0.1%
399.9 3
< 0.1%

Developer
Real number (ℝ)

Distinct3084
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1538.5255
Minimum0
Maximum3083
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-04-18T12:09:24.669336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile151
Q1763
median1543.5
Q32338
95-th percentile2928
Maximum3083
Range3083
Interquartile range (IQR)1575

Descriptive statistics

Standard deviation887.42785
Coefficient of variation (CV)0.57680413
Kurtosis-1.2166469
Mean1538.5255
Median Absolute Deviation (MAD)782.5
Skewness-0.0051183803
Sum20536238
Variance787528.19
MonotonicityNot monotonic
2023-04-18T12:09:24.818578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2531 278
 
2.1%
692 96
 
0.7%
1158 75
 
0.6%
2136 69
 
0.5%
763 66
 
0.5%
2374 48
 
0.4%
1521 48
 
0.4%
2218 48
 
0.4%
842 45
 
0.3%
1155 44
 
0.3%
Other values (3074) 12531
93.9%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 2
 
< 0.1%
2 3
 
< 0.1%
3 3
 
< 0.1%
4 3
 
< 0.1%
5 12
0.1%
6 3
 
< 0.1%
7 1
 
< 0.1%
8 3
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
3083 5
< 0.1%
3082 3
 
< 0.1%
3081 2
 
< 0.1%
3080 3
 
< 0.1%
3079 9
0.1%
3078 2
 
< 0.1%
3077 6
< 0.1%
3076 3
 
< 0.1%
3075 4
< 0.1%
3074 7
0.1%

Age Rating
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
4
8243 
9
2466 
12
2202 
17
 
437

Length

Max length2
Median length1
Mean length1.1977075
Min length1

Characters and Unicode

Total characters15987
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row12
3rd row12
4th row12
5th row12

Common Values

ValueCountFrequency (%)
4 8243
61.8%
9 2466
 
18.5%
12 2202
 
16.5%
17 437
 
3.3%

Length

2023-04-18T12:09:24.958222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:25.105932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
4 8243
61.8%
9 2466
 
18.5%
12 2202
 
16.5%
17 437
 
3.3%

Most occurring characters

ValueCountFrequency (%)
4 8243
51.6%
1 2639
 
16.5%
9 2466
 
15.4%
2 2202
 
13.8%
7 437
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15987
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 8243
51.6%
1 2639
 
16.5%
9 2466
 
15.4%
2 2202
 
13.8%
7 437
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15987
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 8243
51.6%
1 2639
 
16.5%
9 2466
 
15.4%
2 2202
 
13.8%
7 437
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15987
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 8243
51.6%
1 2639
 
16.5%
9 2466
 
15.4%
2 2202
 
13.8%
7 437
 
2.7%

Languages
Real number (ℝ)

Distinct580
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215.94171
Minimum0
Maximum579
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-04-18T12:09:25.250608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile115
Q1173
median173
Q3186
95-th percentile437
Maximum579
Range579
Interquartile range (IQR)13

Descriptive statistics

Standard deviation107.02384
Coefficient of variation (CV)0.4956145
Kurtosis1.3879923
Mean215.94171
Median Absolute Deviation (MAD)0
Skewness1.3950653
Sum2882390
Variance11454.103
MonotonicityNot monotonic
2023-04-18T12:09:25.396729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
173 8774
65.7%
435 272
 
2.0%
437 229
 
1.7%
12 136
 
1.0%
175 134
 
1.0%
428 113
 
0.8%
134 109
 
0.8%
401 108
 
0.8%
418 105
 
0.8%
410 96
 
0.7%
Other values (570) 3272
 
24.5%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 2
 
< 0.1%
2 2
 
< 0.1%
3 2
 
< 0.1%
4 3
 
< 0.1%
5 6
 
< 0.1%
6 5
 
< 0.1%
7 29
0.2%
8 6
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
579 3
< 0.1%
578 2
 
< 0.1%
577 2
 
< 0.1%
576 2
 
< 0.1%
575 3
< 0.1%
574 3
< 0.1%
573 2
 
< 0.1%
572 3
< 0.1%
571 3
< 0.1%
570 5
< 0.1%

Size
Real number (ℝ)

Distinct5081
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3379002 × 108
Minimum215840
Maximum4.005591 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.6 KiB
2023-04-18T12:09:25.556990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum215840
5-th percentile7464960
Q126783744
median64689664
Q31.5419802 × 108
95-th percentile4.4353434 × 108
Maximum4.005591 × 109
Range4.0053752 × 109
Interquartile range (IQR)1.2741427 × 108

Descriptive statistics

Standard deviation2.4371299 × 108
Coefficient of variation (CV)1.8216081
Kurtosis79.69544
Mean1.3379002 × 108
Median Absolute Deviation (MAD)46152192
Skewness7.2959275
Sum1.7858292 × 1012
Variance5.9396019 × 1016
MonotonicityNot monotonic
2023-04-18T12:09:25.720555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92348416 9
 
0.1%
12455936 7
 
0.1%
35073024 6
 
< 0.1%
52236288 6
 
< 0.1%
50012160 6
 
< 0.1%
45379584 6
 
< 0.1%
6000640 6
 
< 0.1%
29645824 6
 
< 0.1%
14524416 6
 
< 0.1%
38862848 6
 
< 0.1%
Other values (5071) 13284
99.5%
ValueCountFrequency (%)
215840 3
< 0.1%
289805 2
< 0.1%
318018 3
< 0.1%
356352 3
< 0.1%
409570 3
< 0.1%
413902 3
< 0.1%
520192 3
< 0.1%
674816 2
< 0.1%
681885 2
< 0.1%
731525 3
< 0.1%
ValueCountFrequency (%)
4005591040 2
< 0.1%
3916692480 2
< 0.1%
3747742720 2
< 0.1%
3716897792 3
< 0.1%
3599435776 2
< 0.1%
3518277632 2
< 0.1%
3321082880 3
< 0.1%
3181755392 3
< 0.1%
2796224512 3
< 0.1%
2581730304 2
< 0.1%

Primary Genre
Real number (ℝ)

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0842074
Minimum0
Maximum18
Zeros12
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2023-04-18T12:09:25.871625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q16
median6
Q36
95-th percentile6
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4106423
Coefficient of variation (CV)0.23185309
Kurtosis47.975731
Mean6.0842074
Median Absolute Deviation (MAD)0
Skewness5.9773565
Sum81212
Variance1.9899117
MonotonicityNot monotonic
2023-04-18T12:09:25.992056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
6 12775
95.7%
3 209
 
1.6%
18 91
 
0.7%
2 86
 
0.6%
16 51
 
0.4%
13 34
 
0.3%
4 18
 
0.1%
12 18
 
0.1%
0 12
 
0.1%
17 10
 
0.1%
Other values (9) 44
 
0.3%
ValueCountFrequency (%)
0 12
 
0.1%
1 4
 
< 0.1%
2 86
 
0.6%
3 209
 
1.6%
4 18
 
0.1%
5 3
 
< 0.1%
6 12775
95.7%
7 6
 
< 0.1%
8 6
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
18 91
0.7%
17 10
 
0.1%
16 51
0.4%
15 7
 
0.1%
14 3
 
< 0.1%
13 34
 
0.3%
12 18
 
0.1%
11 6
 
< 0.1%
10 6
 
< 0.1%
9 3
 
< 0.1%

Original Release Date
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6785286
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.6 KiB
2023-04-18T12:09:26.116640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4807971
Coefficient of variation (CV)0.52119222
Kurtosis-1.231049
Mean6.6785286
Median Absolute Deviation (MAD)3
Skewness-0.050025922
Sum89145
Variance12.115949
MonotonicityNot monotonic
2023-04-18T12:09:26.218600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 1291
9.7%
9 1238
9.3%
11 1202
9.0%
3 1155
8.7%
6 1116
8.4%
4 1114
8.3%
8 1083
8.1%
1 1068
8.0%
10 1067
8.0%
5 1044
7.8%
Other values (2) 1970
14.8%
ValueCountFrequency (%)
1 1068
8.0%
2 948
7.1%
3 1155
8.7%
4 1114
8.3%
5 1044
7.8%
6 1116
8.4%
7 1022
7.7%
8 1083
8.1%
9 1238
9.3%
10 1067
8.0%
ValueCountFrequency (%)
12 1291
9.7%
11 1202
9.0%
10 1067
8.0%
9 1238
9.3%
8 1083
8.1%
7 1022
7.7%
6 1116
8.4%
5 1044
7.8%
4 1114
8.3%
3 1155
8.7%
Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.572745
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.6 KiB
2023-04-18T12:09:26.333118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.309562
Coefficient of variation (CV)0.50352812
Kurtosis-1.075874
Mean6.572745
Median Absolute Deviation (MAD)3
Skewness-0.0412677
Sum87733
Variance10.9532
MonotonicityNot monotonic
2023-04-18T12:09:26.437828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 1701
12.7%
6 1236
9.3%
10 1158
8.7%
5 1151
8.6%
9 1123
8.4%
4 1070
8.0%
1 1025
7.7%
12 1015
7.6%
11 1000
7.5%
8 965
7.2%
Other values (2) 1904
14.3%
ValueCountFrequency (%)
1 1025
7.7%
2 949
7.1%
3 955
7.2%
4 1070
8.0%
5 1151
8.6%
6 1236
9.3%
7 1701
12.7%
8 965
7.2%
9 1123
8.4%
10 1158
8.7%
ValueCountFrequency (%)
12 1015
7.6%
11 1000
7.5%
10 1158
8.7%
9 1123
8.4%
8 965
7.2%
7 1701
12.7%
6 1236
9.3%
5 1151
8.6%
4 1070
8.0%
3 955
7.2%

Average_User_Rating
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0289931
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.6 KiB
2023-04-18T12:09:26.554052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q13.5
median4
Q34.5
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7520497
Coefficient of variation (CV)0.18665947
Kurtosis1.2331703
Mean4.0289931
Median Absolute Deviation (MAD)0.5
Skewness-1.1591655
Sum53779
Variance0.56557876
MonotonicityNot monotonic
2023-04-18T12:09:26.669024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4.5 5003
37.5%
4.0 3226
24.2%
3.5 1666
 
12.5%
5.0 1492
 
11.2%
3.0 950
 
7.1%
2.5 572
 
4.3%
2.0 292
 
2.2%
1.5 119
 
0.9%
1.0 28
 
0.2%
ValueCountFrequency (%)
1.0 28
 
0.2%
1.5 119
 
0.9%
2.0 292
 
2.2%
2.5 572
 
4.3%
3.0 950
 
7.1%
3.5 1666
 
12.5%
4.0 3226
24.2%
4.5 5003
37.5%
5.0 1492
 
11.2%
ValueCountFrequency (%)
5.0 1492
 
11.2%
4.5 5003
37.5%
4.0 3226
24.2%
3.5 1666
 
12.5%
3.0 950
 
7.1%
2.5 572
 
4.3%
2.0 292
 
2.2%
1.5 119
 
0.9%
1.0 28
 
0.2%

Genres_Action
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
12645 
1
 
703

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12645
94.7%
1 703
 
5.3%

Length

2023-04-18T12:09:26.794763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:26.936044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 12645
94.7%
1 703
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 12645
94.7%
1 703
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12645
94.7%
1 703
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12645
94.7%
1 703
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12645
94.7%
1 703
 
5.3%

Genres_Adventure
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13111 
1
 
237

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13111
98.2%
1 237
 
1.8%

Length

2023-04-18T12:09:27.061758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:27.185689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13111
98.2%
1 237
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 13111
98.2%
1 237
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13111
98.2%
1 237
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13111
98.2%
1 237
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13111
98.2%
1 237
 
1.8%

Genres_Board
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
12734 
1
 
614

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12734
95.4%
1 614
 
4.6%

Length

2023-04-18T12:09:27.282250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:27.411665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 12734
95.4%
1 614
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 12734
95.4%
1 614
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12734
95.4%
1 614
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12734
95.4%
1 614
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12734
95.4%
1 614
 
4.6%

Genres_Books
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13342 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%

Length

2023-04-18T12:09:27.509764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:27.634791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%

Genres_Business
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13342 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%

Length

2023-04-18T12:09:27.740914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:27.865036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%

Genres_Card
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13097 
1
 
251

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13097
98.1%
1 251
 
1.9%

Length

2023-04-18T12:09:27.959441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:28.079700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13097
98.1%
1 251
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 13097
98.1%
1 251
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13097
98.1%
1 251
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13097
98.1%
1 251
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13097
98.1%
1 251
 
1.9%

Genres_Casino
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13325 
1
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13325
99.8%
1 23
 
0.2%

Length

2023-04-18T12:09:28.192304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:28.324587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13325
99.8%
1 23
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 13325
99.8%
1 23
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13325
99.8%
1 23
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13325
99.8%
1 23
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13325
99.8%
1 23
 
0.2%

Genres_Casual
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13006 
1
 
342

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13006
97.4%
1 342
 
2.6%

Length

2023-04-18T12:09:28.421592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:28.542127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13006
97.4%
1 342
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 13006
97.4%
1 342
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13006
97.4%
1 342
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13006
97.4%
1 342
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13006
97.4%
1 342
 
2.6%

Genres_Education
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13188 
1
 
160

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13188
98.8%
1 160
 
1.2%

Length

2023-04-18T12:09:28.641784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:28.762598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13188
98.8%
1 160
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 13188
98.8%
1 160
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13188
98.8%
1 160
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13188
98.8%
1 160
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13188
98.8%
1 160
 
1.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13347 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13347
> 99.9%
1 1
 
< 0.1%

Length

2023-04-18T12:09:28.861939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:28.979666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13347
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13347
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13347
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13347
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13347
> 99.9%
1 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
10765 
1
2583 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10765
80.6%
1 2583
 
19.4%

Length

2023-04-18T12:09:29.213530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:29.330600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 10765
80.6%
1 2583
 
19.4%

Most occurring characters

ValueCountFrequency (%)
0 10765
80.6%
1 2583
 
19.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10765
80.6%
1 2583
 
19.4%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10765
80.6%
1 2583
 
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10765
80.6%
1 2583
 
19.4%

Genres_Family
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13136 
1
 
212

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13136
98.4%
1 212
 
1.6%

Length

2023-04-18T12:09:29.429781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:29.548542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13136
98.4%
1 212
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 13136
98.4%
1 212
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13136
98.4%
1 212
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13136
98.4%
1 212
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13136
98.4%
1 212
 
1.6%

Genres_Finance
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13336 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13336
99.9%
1 12
 
0.1%

Length

2023-04-18T12:09:29.655288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:29.782839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13336
99.9%
1 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 13336
99.9%
1 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13336
99.9%
1 12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13336
99.9%
1 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13336
99.9%
1 12
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13332 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13332
99.9%
1 16
 
0.1%

Length

2023-04-18T12:09:29.884941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:30.021141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13332
99.9%
1 16
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 13332
99.9%
1 16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13332
99.9%
1 16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13332
99.9%
1 16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13332
99.9%
1 16
 
0.1%

Genres_Games
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13149 
1
 
199

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13149
98.5%
1 199
 
1.5%

Length

2023-04-18T12:09:30.146633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:30.264642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13149
98.5%
1 199
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 13149
98.5%
1 199
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13149
98.5%
1 199
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13149
98.5%
1 199
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13149
98.5%
1 199
 
1.5%

Genres_Gaming
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13346 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%

Length

2023-04-18T12:09:30.386585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:30.538993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13338 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13338
99.9%
1 10
 
0.1%

Length

2023-04-18T12:09:30.649355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:30.779906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13338
99.9%
1 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 13338
99.9%
1 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13338
99.9%
1 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13338
99.9%
1 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13338
99.9%
1 10
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13347 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13347
> 99.9%
1 1
 
< 0.1%

Length

2023-04-18T12:09:30.876063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:30.998468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13347
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13347
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13347
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13347
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13347
> 99.9%
1 1
 
< 0.1%

Genres_Lifestyle
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13295 
1
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13295
99.6%
1 53
 
0.4%

Length

2023-04-18T12:09:31.094915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:31.219873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13295
99.6%
1 53
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 13295
99.6%
1 53
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13295
99.6%
1 53
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13295
99.6%
1 53
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13295
99.6%
1 53
 
0.4%

Genres_Medical
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13346 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%

Length

2023-04-18T12:09:31.331987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:31.466385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%

Genres_Music
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13317 
1
 
31

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13317
99.8%
1 31
 
0.2%

Length

2023-04-18T12:09:31.580606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:31.724766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13317
99.8%
1 31
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 13317
99.8%
1 31
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13317
99.8%
1 31
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13317
99.8%
1 31
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13317
99.8%
1 31
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13346 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%

Length

2023-04-18T12:09:31.838992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:31.963631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13346
> 99.9%
1 2
 
< 0.1%

Genres_News
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13341 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 13341
99.9%
1 7
 
0.1%

Length

2023-04-18T12:09:32.063554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:32.192688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13341
99.9%
1 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 13341
99.9%
1 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13341
99.9%
1 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13341
99.9%
1 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13341
99.9%
1 7
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13342 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%

Length

2023-04-18T12:09:32.289720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:32.412004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13342
> 99.9%
1 6
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13339 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13339
99.9%
1 9
 
0.1%

Length

2023-04-18T12:09:32.509077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:32.630841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13339
99.9%
1 9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 13339
99.9%
1 9
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13339
99.9%
1 9
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13339
99.9%
1 9
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13339
99.9%
1 9
 
0.1%

Genres_Puzzle
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
12363 
1
 
985

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12363
92.6%
1 985
 
7.4%

Length

2023-04-18T12:09:32.726843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:32.847522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 12363
92.6%
1 985
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 12363
92.6%
1 985
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12363
92.6%
1 985
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12363
92.6%
1 985
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12363
92.6%
1 985
 
7.4%

Genres_Racing
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13316 
1
 
32

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13316
99.8%
1 32
 
0.2%

Length

2023-04-18T12:09:32.943541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:33.061675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13316
99.8%
1 32
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 13316
99.8%
1 32
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13316
99.8%
1 32
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13316
99.8%
1 32
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13316
99.8%
1 32
 
0.2%

Genres_Reference
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13329 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13329
99.9%
1 19
 
0.1%

Length

2023-04-18T12:09:33.163363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:33.282640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13329
99.9%
1 19
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 13329
99.9%
1 19
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13329
99.9%
1 19
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13329
99.9%
1 19
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13329
99.9%
1 19
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
12795 
1
 
553

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12795
95.9%
1 553
 
4.1%

Length

2023-04-18T12:09:33.379864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:33.495881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 12795
95.9%
1 553
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 12795
95.9%
1 553
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12795
95.9%
1 553
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12795
95.9%
1 553
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12795
95.9%
1 553
 
4.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
12559 
1
 
789

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12559
94.1%
1 789
 
5.9%

Length

2023-04-18T12:09:33.592859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:33.716060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 12559
94.1%
1 789
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 12559
94.1%
1 789
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12559
94.1%
1 789
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12559
94.1%
1 789
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12559
94.1%
1 789
 
5.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13306 
1
 
42

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13306
99.7%
1 42
 
0.3%

Length

2023-04-18T12:09:33.813468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:33.932876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13306
99.7%
1 42
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 13306
99.7%
1 42
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13306
99.7%
1 42
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13306
99.7%
1 42
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13306
99.7%
1 42
 
0.3%

Genres_Sports
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13221 
1
 
127

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13221
99.0%
1 127
 
1.0%

Length

2023-04-18T12:09:34.027456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:34.147494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13221
99.0%
1 127
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 13221
99.0%
1 127
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13221
99.0%
1 127
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13221
99.0%
1 127
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13221
99.0%
1 127
 
1.0%

Genres_Strategy
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
8177 
1
5171 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 8177
61.3%
1 5171
38.7%

Length

2023-04-18T12:09:34.241865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:34.362500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 8177
61.3%
1 5171
38.7%

Most occurring characters

ValueCountFrequency (%)
0 8177
61.3%
1 5171
38.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8177
61.3%
1 5171
38.7%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8177
61.3%
1 5171
38.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8177
61.3%
1 5171
38.7%

Genres_Travel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13319 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13319
99.8%
1 29
 
0.2%

Length

2023-04-18T12:09:34.460695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:34.578648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13319
99.8%
1 29
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 13319
99.8%
1 29
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13319
99.8%
1 29
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13319
99.8%
1 29
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13319
99.8%
1 29
 
0.2%

Genres_Trivia
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13287 
1
 
61

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13287
99.5%
1 61
 
0.5%

Length

2023-04-18T12:09:34.674699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:34.790809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13287
99.5%
1 61
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 13287
99.5%
1 61
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13287
99.5%
1 61
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13287
99.5%
1 61
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13287
99.5%
1 61
 
0.5%

Genres_Utilities
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13326 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13326
99.8%
1 22
 
0.2%

Length

2023-04-18T12:09:34.885857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:35.003833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13326
99.8%
1 22
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 13326
99.8%
1 22
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13326
99.8%
1 22
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13326
99.8%
1 22
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13326
99.8%
1 22
 
0.2%

Genres_Word
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.6 KiB
0
13318 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13348
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13318
99.8%
1 30
 
0.2%

Length

2023-04-18T12:09:35.103981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-18T12:09:35.230288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13318
99.8%
1 30
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 13318
99.8%
1 30
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13318
99.8%
1 30
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 13348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13318
99.8%
1 30
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13318
99.8%
1 30
 
0.2%

Interactions

2023-04-18T12:09:20.875777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:08.821677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:10.259213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:11.584162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:12.897051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:14.279745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:15.566697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:16.925174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:18.252605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:19.625201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:21.014328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:08.982089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:10.390364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:11.713773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:13.026750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:14.406114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:15.705197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:17.059297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:18.379955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:19.746906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:21.149167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:09.196655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:10.520061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:11.846318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:13.154284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:14.536805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:15.838079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:17.189121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:18.502656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:19.879478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:21.286992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:09.328650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:10.650114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:11.976805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:13.280425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:14.666181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:15.974054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:17.324797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:18.627925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:20.005195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:21.417756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:09.457868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:10.796840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:12.102056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:13.406315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:14.791863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:16.106997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:17.452750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:18.749127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:20.127972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:21.548300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:09.587835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:10.926236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:12.228906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:13.531703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:14.918171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:16.247931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:17.587615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:18.872930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:20.250768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:21.689555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:09.726969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:11.063684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:12.370177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:13.664227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:15.051895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:16.383672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:17.728006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:19.013739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:20.385230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:21.820679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:09.864659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:11.196234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:12.502562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:13.792997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:15.179073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:16.525607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:17.858653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:19.142075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:20.511187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:21.946096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:09.993732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:11.323190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:12.626154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:13.911202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:15.296674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:16.653070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:17.984021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:19.254835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:20.624190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:22.072077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:10.115296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:11.448212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:12.760149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:14.031641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:15.422885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:16.780139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:18.108927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:19.372740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-18T12:09:20.740045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Missing values

2023-04-18T12:09:22.382458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-18T12:09:23.218102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

User Rating CountPriceIn-app PurchasesDeveloperAge RatingLanguagesSizePrimary GenreOriginal Release DateCurrent Version Release DateAverage_User_RatingGenres_ActionGenres_AdventureGenres_BoardGenres_BooksGenres_BusinessGenres_CardGenres_CasinoGenres_CasualGenres_EducationGenres_Emoji&ExpressionsGenres_EntertainmentGenres_FamilyGenres_FinanceGenres_Food&DrinkGenres_GamesGenres_GamingGenres_Health&FitnessGenres_Kids&FamilyGenres_LifestyleGenres_MedicalGenres_MusicGenres_NavigationGenres_NewsGenres_Photo&VideoGenres_ProductivityGenres_PuzzleGenres_RacingGenres_ReferenceGenres_RolePlayingGenres_SimulationGenres_SocialNetworkingGenres_SportsGenres_StrategyGenres_TravelGenres_TriviaGenres_UtilitiesGenres_Word
09820.0307.84962123268944896006374.00000000000000000000000000000100000000
09820.0307.84962123268944896006374.00000000000000000000000000000000010000
1190.00.00273121731164072966363.50000000000000000000000000000010000000
1190.00.00273121731164072966363.50000000000000000000000000000000010000
1190.00.00273121731164072966363.50000000000000000000000100000000000000
2140.00.005064435506470406444.50000000000000000000000000000000010000
2140.00.005064435506470406444.50000000000100000000000000000000000000
2140.00.005064435506470406444.50000010000000000000000000000000000000
3881.990.009669173281200646573.50000000000000000000000000000000000010
3881.990.009669173281200646573.50000010000000000000000000000000000000
User Rating CountPriceIn-app PurchasesDeveloperAge RatingLanguagesSizePrimary GenreOriginal Release DateCurrent Version Release DateAverage_User_RatingGenres_ActionGenres_AdventureGenres_BoardGenres_BooksGenres_BusinessGenres_CardGenres_CasinoGenres_CasualGenres_EducationGenres_Emoji&ExpressionsGenres_EntertainmentGenres_FamilyGenres_FinanceGenres_Food&DrinkGenres_GamesGenres_GamingGenres_Health&FitnessGenres_Kids&FamilyGenres_LifestyleGenres_MedicalGenres_MusicGenres_NavigationGenres_NewsGenres_Photo&VideoGenres_ProductivityGenres_PuzzleGenres_RacingGenres_ReferenceGenres_RolePlayingGenres_SimulationGenres_SocialNetworkingGenres_SportsGenres_StrategyGenres_TravelGenres_TriviaGenres_UtilitiesGenres_Word
52102520.060.92534402792678406144.50000000000000000000000000000000010000
52102520.060.92534402792678406144.50000000000100000000000000000000000000
5211120.00.00242341731951334418825.00000000000000010000000000000000000000
5211120.00.00242341731951334418825.00010000000000000000000000000000000000
5211120.00.00242341731951334418825.00000000000000000000000000000000010000
52125780.0121.90286691732756802566614.00000000000000000000000000000000010000
52125780.0121.90286691732756802566614.00000000000000000000000000000100000000
52137820.088.9324754173854384646933.50000010000000000000000000000000000000
52137820.088.9324754173854384646933.50000000000100000000000000000000000000
52137820.088.9324754173854384646933.50000000000000000000000000000000010000